During training stage, the ground truth center point \(p\in \mathcal {R}^2\)  is converted from bounding box and further mapped to a low-resolution equivalent that is \(\tilde{p}=\lfloor {p/R}\rfloor \) . Then the ground truth center point is splat to a heatmap version \(Y\in [0,1]^{{W/R}\times {H/R}\times {C}}\)  using a Gaussian kernel as it does in [1]}. The ground truth of object size is computed as \(s_k=(a^{k}_2 - a^{k}_1, b_2^{k} - b_1^{k})\) . To optimize the center point heatmap prediction network, we use a penalty-reduced pixel-wise logistic regression with focal loss [2]} following [3]}:
